Reihaneh Rabbany

Biography

Reihaneh Rabbany is an assistant professor at the School of Computer Science, McGill University, and a core academic member of Mila – Quebec Artificial Intelligence Institute. She is also a Canada CIFAR AI Chair and on the faculty of McGill’s Centre for the Study of Democratic Citizenship.

Before joining McGill, Rabbany was a postdoctoral fellow at the School of Computer Science, Carnegie Mellon University. She completed her PhD in the Department of Computing Science at the University of Alberta.

Rabbany heads McGill’s Complex Data Lab, where she conducts research at the intersection of network science, data mining and machine learning, with a focus on analyzing real-world interconnected data and social good applications.

Current Students

Hussein Abdallah

Postdoctorate - McGill University

Taimaa Bachi

Research Intern - McGill University

Aurélien Bück-Kaeffer

Master's Research - McGill University

Jacob Chmura

Master's Research - McGill University

Principal supervisor :

Research Intern - Université de Montréal

Principal supervisor :

Islam Eldifrawi

Independent visiting researcher - University of Sherbrooke

Aarash Feizi

PhD - McGill University

Co-supervisor :

Adriana Romero Soriano

Collaborating Alumni - McGill University

Co-supervisor :

Anne Imouza

PhD - McGill University

Principal supervisor :

Yann Klein

Research Intern - McGill University University

Emma Kondrup

PhD - McGill University

Co-supervisor :

PhD - McGill University

Sitao Luan

Postdoctorate - McGill University

Principal supervisor :

Guillaume Rabusseau

Shahrad Mohammadzadeh

Master's Research - McGill University

Co-supervisor :

Independent visiting researcher - McGill University

Collaborating Alumni - McGill University

Soroush Omranpour

Collaborating Alumni - McGill University

Co-supervisor :

Guillaume Rabusseau

Farimah Poursafaei

Collaborating Alumni - McGill University

Research Intern - McGill University

Sebastian Sabry

Master's Research - McGill University University

Dorsaf Sallami

Research Intern - McGill University

Sneheel Sarangi

Master's Research - McGill University

Master's Research - McGill University

Camille Thibault

Master's Research - Université de Montréal

Principal supervisor :

Collaborating researcher - McGill University

Collaborating researcher - Université de Montréal

Principal supervisor :

PhD - McGill University

Sveta Zhuk

Master's Research - Université de Montréal

Principal supervisor :

Unmasking deepfakes with AI

Blog Posts

Un groupe hétéroclite de huit jeunes adultes se tient serré sur un toit, souriant et riant avec la silhouette de la ville en arrière-plan. Deux encarts circulaires mettent en évidence une caméra vintage tenue par l'un des membres du groupe, soulignant l'élément deepfake.

December 16, 2025

Victor Livernoche

Reihaneh Rabbany

Read the article

August 3, 2021

Flight-SEIR: Incorporating Flight Data to Improve Epidemiological Modelling and Disease Outbreak Prevention

Shenyang Huang

Reihaneh Rabbany

Read the article

Publications

Comparing GPT-4 and Open-Source Language Models in Misinformation Mitigation

Tyler Vergho

Recent large language models (LLMs) have been shown to be effective for misinformation detection. However, the choice of LLMs for experiment… (see more)s varies widely, leading to uncertain conclusions. In particular, GPT-4 is known to be strong in this domain, but it is closed source, potentially expensive, and can show instability between different versions. Meanwhile, alternative LLMs have given mixed results. In this work, we show that Zephyr-7b presents a consistently viable alternative, overcoming key limitations of commonly used approaches like Llama-2 and GPT-3.5. This provides the research community with a solid open-source option and shows open-source models are gradually catching up on this task. We then highlight how GPT-3.5 exhibits unstable performance, such that this very widely used model could provide misleading results in misinformation detection. Finally, we validate new tools including approaches to structured output and the latest version of GPT-4 (Turbo), showing they do not compromise performance, thus unlocking them for future research and potentially enabling more complex pipelines for misinformation mitigation.

2024-01-11

ArXiv (preprint)

GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning

Aarash Feizi

Randall Balestriero

Arantxa Casanova

Adriana Romero

We propose Guided Positive Sampling Self-Supervised Learning (GPS-SSL), a general method to inject a priori knowledge into Self-Supervised L… (see more)earning (SSL) positive samples selection. Current SSL methods leverage Data-Augmentations (DA) for generating positive samples and incorporate prior knowledge - an incorrect, or too weak DA will drastically reduce the quality of the learned representation. GPS-SSL proposes instead to design a metric space where Euclidean distances become a meaningful proxy for semantic relationship. In that space, it is now possible to generate positive samples from nearest neighbor sampling. Any prior knowledge can now be embedded into that metric space independently from the employed DA. From its simplicity, GPS-SSL is applicable to any SSL method, e.g. SimCLR or BYOL. A key benefit of GPS-SSL is in reducing the pressure in tailoring strong DAs. For example GPS-SSL reaches 85.58% on Cifar10 with weak DA while the baseline only reaches 37.51%. We therefore move a step forward towards the goal of making SSL less reliant on DA. We also show that even when using strong DAs, GPS-SSL outperforms the baselines on under-studied domains. We evaluate GPS-SSL along with multiple baseline SSL methods on numerous downstream datasets from different domains when the models use strong or minimal data augmentations. We hope that GPS-SSL will open new avenues in studying how to inject a priori knowledge into SSL in a principled manner.

2024-01-02

ArXiv (preprint)

openreview.net

Uncertainty Resolution in Misinformation Detection

Yury Orlovskiy

Camille Thibault

Anne Imouza

2024-01-01

ArXiv (preprint)

An Evaluation of Language Models for Hyperpartisan Ideology Detection in Persian Twitter

Sahar Omidi Shayegan

Isar Nejadgholi

Hao Yu

Sacha Lévy

Zachary Yang

Large Language Models (LLMs) have shown significant promise in various tasks, including identifying the political beliefs of English-speakin… (see more)g social media users from their posts. However, assessing LLMs for this task in non-English languages remains unexplored. In this work, we ask to what extent LLMs can predict the political ideologies of users in Persian social media. To answer this question, we first acknowledge that political parties are not well-defined among Persian users, and therefore, we simplify the task to a much simpler task of hyperpartisan ideology detection. We create a new benchmark and show the potential and limitations of both open-source and commercial LLMs in classifying the hyper-partisan ideologies of users. We compare these models with smaller fine-tuned models, both on the Persian language (ParsBERT) and translated data (RoBERTa), showing that they considerably outperform generative LLMs in this task. We further demonstrate that the performance of the generative LLMs degrades when classifying users based on their tweets instead of their bios and even when tweets are added as additional information, whereas the smaller fine-tuned models are robust and achieve similar performance for all classes. This study is a first step toward political ideology detection in Persian Twitter, with implications for future research to understand the dynamics of ideologies in Persian social media.

2023-12-31

EURALI (published)

www.semanticscholar.org

Game On, Hate Off: A Study of Toxicity in Online Multiplayer Environments.

Zachary Yang

Nicolas Grenon-Godbout

The advent of online spaces, particularly social media platforms and video games, has brought forth a significant challenge: the detection a… (see more)nd mitigation of toxic and harmful speech. This issue is not only pervasive but also detrimental to the overall user experience. In this study, we leverage small language models to reliably detect toxicity, achieving an average precision of 0.95. Analyzing eight months of chat data from two Ubisoft games, we uncover patterns and trends in toxic behavior. The insights derived from our research will contribute to the development of healthier online communities and inform preventive measures against toxicity.

2023-12-31

Games Res. Pract. (published)

Quantifying learning-style adaptation in effectiveness of LLM teaching

Ruben Weijers

Gabrielle Fidelis de Castilho

This preliminary study aims to investigate whether AI, when prompted based on individual learning styles, can effectively improve comprehens… (see more)ion and learning experiences in educational settings. It involves tailoring LLMs baseline prompts and comparing the results of a control group receiving standard content and an experimental group receiving learning style-tailored content. Preliminary results suggest that GPT-4 can generate responses aligned with various learning styles, indicating the potential for enhanced engagement and comprehension. However, these results also reveal challenges, including the model’s tendency for sycophantic behavior and variability in responses. Our findings suggest that a more sophisticated prompt engineering approach is required for integrating AI into education (AIEd) to improve educational outcomes.

2023-12-31

PERSONALIZE (published)

Temporal Graph Analysis with TGX

Real-world networks, with their evolving relations, are best captured as temporal graphs. However, existing software libraries are largely d… (see more)esigned for static graphs where the dynamic nature of temporal graphs is ignored. Bridging this gap, we introduce TGX, a Python package specially designed for analysis of temporal networks that encompasses an automated pipeline for data loading, data processing, and analysis of evolving graphs. TGX provides access to eleven built-in datasets and eight external Temporal Graph Benchmark (TGB) datasets as well as any novel datasets in the .csv format. Beyond data loading, TGX facilitates data processing functionalities such as discretization of temporal graphs and node subsampling to accelerate working with larger datasets. For comprehensive investigation, TGX offers network analysis by providing a diverse set of measures, including average node degree and the evolving number of nodes and edges per timestamp. Additionally, the package consolidates meaningful visualization plots indicating the evolution of temporal patterns, such as Temporal Edge Appearance (TEA) and Temporal Edge Trafficc (TET) plots. The TGX package is a robust tool for examining the features of temporal graphs and can be used in various areas like studying social networks, citation networks, and tracking user interactions. We plan to continuously support and update TGX based on community feedback. TGX is publicly available on: https://github.com/ComplexData-MILA/TGX.

2023-12-31

WSDM (published)

UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs

Emanuele Rossi

Many real world graphs are inherently dynamic, constantly evolving with node and edge additions. These graphs can be represented by temporal… (see more) graphs, either through a stream of edge events or a sequence of graph snapshots. Until now, the development of machine learning methods for both types has occurred largely in isolation, resulting in limited experimental comparison and theoretical crosspollination between the two. In this paper, we introduce Unified Temporal Graph (UTG), a framework that unifies snapshot-based and event-based machine learning models under a single umbrella, enabling models developed for one representation to be applied effectively to datasets of the other. We also propose a novel UTG training procedure to boost the performance of snapshot-based models in the streaming setting. We comprehensively evaluate both snapshot and event-based models across both types of temporal graphs on the temporal link prediction task. Our main findings are threefold: first, when combined with UTG training, snapshot-based models can perform competitively with event-based models such as TGN and GraphMixer even on event datasets. Second, snapshot-based models are at least an order of magnitude faster than most event-based models during inference. Third, while event-based methods such as NAT and DyGFormer outperforms snapshot-based methods on both types of temporal graphs, this is because they leverage joint neighborhood structural features thus emphasizing the potential to incorporate these features into snapshotbased models as well. These findings highlight the importance of comparing model architectures independent of the data format and suggest the potential of combining the efficiency of snapshot-based models with the performance of event-based models in the future.

2023-12-31

LoG (published)

proceedings.mlr.press

Exhaustive Evaluation of Dynamic Link Prediction

Farimah Poursafaei

Dynamic link prediction is a crucial task in the study of evolving graphs, which serve as abstract models for various real-world application… (see more)s. Recent dynamic graph representation learning models have claimed near-perfect performance in this task. However, we argue that the standard evaluation strategy for dynamic link prediction overlooks the sparsity and recurrence patterns inherent in dynamic networks. Specifically, the current strategy suffers from issues such as evaluating models on a balanced set of positive and negative edges, neglecting the reassessment of frequently recurring positive edges, and lacking a comprehensive evaluation of both recurring and new edges.To address these limitations, we propose a novel evaluation strategy called EXHAUSTIVE, which takes into account all relevant negative edges and separately assesses the performance on recurring and new edges. Using our proposed evaluation strategy, we compare the performance of five state-of-the-art dynamic graph learning models on seven benchmark datasets. Compared to the previous common evaluation strategy, we observe an average drop of 62% in Average Precision for dynamic link prediction. Additionally, the ranking of the models also changes under the new evaluation setting. Furthermore, we demonstrate that while all models perform considerably worse when predicting new edges compared to recurring ones, the best performing models differ between the two scenarios. This highlights the importance of employing the proposed evaluation strategy for both the assessment and design of dynamic link prediction models. By adopting our novel evaluation strategy, researchers can obtain a more accurate understanding of model performance in dynamic link prediction, leading to improved evaluation and design of such models.

2023-12-03

2023 IEEE International Conference on Data Mining Workshops (ICDMW) (published)

SWEET: Weakly Supervised Person Name Extraction for Fighting Human Trafficking

Javin Liu

Hao Yu

2023-11-30

Findings of the Association for Computational Linguistics: EMNLP 2023 (published)

openreview.net

Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs

Samy Coulombe

Dynamic graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly dete… (see more)ction in temporal graphs is crucial for many real world applications such as intrusion identification in network systems, detection of ecosystem disturbances and detection of epidemic outbreaks. In this paper, we focus on change point detection in dynamic graphs and address three main challenges associated with this problem: i). how to compare graph snapshots across time, ii). how to capture temporal dependencies, and iii). how to combine different views of a temporal graph. To solve the above challenges, we first propose Laplacian Anomaly Detection (LAD) which uses the spectrum of graph Laplacian as the low dimensional embedding of the graph structure at each snapshot. LAD explicitly models short term and long term dependencies by applying two sliding windows. Next, we propose MultiLAD, a simple and effective generalization of LAD to multi-view graphs. MultiLAD provides the first change point detection method for multi-view dynamic graphs. It aggregates the singular values of the normalized graph Laplacian from different views through the scalar power mean operation. Through extensive synthetic experiments, we show that i). LAD and MultiLAD are accurate and outperforms state-of-the-art baselines and their multi-view extensions by a large margin, ii). MultiLAD's advantage over contenders significantly increases when additional views are available, and iii). MultiLAD is highly robust to noise from individual views. In five real world dynamic graphs, we demonstrate that LAD and MultiLAD identify significant events as top anomalies such as the implementation of government COVID-19 interventions which impacted the population mobility in multi-view traffic networks.

2023-11-05

ACM Transactions on Knowledge Discovery from Data (published)

Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4

Anne Imouza

Meilina Reksoprodjo

Camille Thibault

Caleb Gupta

Joel Christoph